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Parametric logarithmic type image processing for contrast based auto-focus in extreme lighting conditions

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Florea C., Florea L.
International Journal of Applied Mathematics and Computer Science
|
2013
|
tom 23
|
nr 3
637-648
EN
While most of state-of-the-art image processing techniques were built under the so-called classical linear image processing, an alternative that presents superior behavior for specific applications comes in the form of Logarithmic Type Image Processing (LTIP). This refers to mathematical models constructed for the representation and processing of gray tones images. In this paper we describe a general mathematical framework that allows extensions of these models by various means while preserving their mathematical properties. We propose a parametric extension of LTIP models and discuss its similarities with the human visual system. The usability of the proposed extension model is verified for an application of contrast based auto-focus in extreme lighting conditions. The closing property of the named models facilitates superior behavior when compared with state-of-the-art methods.
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High dynamic range imaging by perceptual logarithmic exposure merging

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Florea C., Vertan C., Florea L.
International Journal of Applied Mathematics and Computer Science
|
2015
|
tom 25
|
nr 4
943-954
EN
In this paper we emphasize a similarity between the logarithmic type image processing (LTIP) model and the Naka-Rushton model of the human visual system (HVS). LTIP is a derivation of logarithmic image processing (LIP), which further replaces the logarithmic function with a ratio of polynomial functions. Based on this similarity, we show that it is possible to present a unifying framework for the high dynamic range (HDR) imaging problem, namely, that performing exposure merging under the LTIP model is equivalent to standard irradiance map fusion. The resulting HDR algorithm is shown to provide high quality in both subjective and objective evaluations.
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